Factors influencing behavioral intention to use adaptive learning systems: Integrating self-determination theory and the unified model of technology acceptance.

This study aims to examine behavioral interest in GoPay user students in the Province of the Special Region of Yogyakarta with the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model, perceived risk, and trust. This research is expected to contribute to solving the problem of GoPay user interest, developing the GoPay system, accelerating the cashless society in Indonesia, and slowing the spread of the Corona virus. This study uses a survey method for distributing questionnaires online. Respondents in this study amounted to 321 students. The sampling technique was carried out using the snowball sampling technique. The data analysis tool in this study uses SmartPLS 3.3.3. The result of this study is that the behavioral intention of students to use GoPay has a positive effect on social influence, hedonic motivation, habit, and trust. On the other hand, performance expectancy, effort expectancy, facilitating conditions, and price value do not have a positive effect on the behavioral intention of students using GoPay in the Special Region of Yogyakarta. Perceived risk does not have a negative effect on the behavioral intention of students using GoPay in the Special Region of Yogyakarta.

This study explores the acceptance and use of artificial-intelligence-powered adaptive learning systems among higher education instructors, utilising the technology adoption decision and use framework and the unified theory of acceptance and use of technology. A survey of 150 instructors was analysed using partial least squares structural equation modelling. The model explained 62.9% of the variance in behavioural intention (R2 = .629, Q2 = .542, f2 = .526) and 4.1% in user behaviour (R2 = .041, Q2 = .001, f2 = .030). Performance expectancy (β = .679, p < .001) significantly influenced behavioural intention, which in turn affected user behaviour (β = -.088, p = .005). The total indirect effect of performance expectancy on user behaviour through behavioural intention was also significant (β = -.060, p = .006). However, factors such as effort expectancy and facilitating conditions demonstrated limited influence. The study emphasises the critical role of perceived performance benefits in driving artificial intelligence adoption and suggests practical implications for policy adjustments to support technology integration in higher education.

The present study develops motivational framework to investigate employee behaviour towards work engagement with the integration of self-regulated theory and self-determination theory. For inferential analysis, structural equation modelling (SEM) approach is applied. Results of the SEM revealed that altogether self-reward strategies, self-goal settings, self-talk, natural reward strategies, perceived autonomy, perceived competency and perceived relatedness explained substantial variance R2 76.9% in measuring employee engagement at workplace. The moderating role of intrinsic motivation is confirmed in such a way that the relationship between employee work engagement and organisational performance will be stronger when intrinsic motivation is higher. Finding of the effect size analysis (f2) established that perceived autonomy had the highest effect size in determining employee work engagement. Therefore, importance performance matrix analysis revealed that managers and policymakers must pay attention to employee perceived autonomy, employee intrinsic motivation and employee work engagement to boost organisational performance.

One of the challenges in adaptive learning systems is to manage learner models that include learners' profiles and activities and interaction between learners and the learning tools. In the context of lifelong learning, the learner model is a user learning pattern, which can be modelled all the time based on user interaction with various sources. The main issue in lifelong learner model design is what type of information needs to be stored in the learner model and how the information granularity is involved. The type and level of information granularity of a user will determine how appropriate the pedagogical policies are to be provided by the system for a user. Lifelong learner model design should be in line with the principles of lifelong learning, namely sociality, interoperability, and scrutability. This paper discusses our research on learner model ontology for lifelong learning. The goal expected to be achieved is that there is a lifelong learner model ontology to be accessed and updated by adaptive learning systems. This research classifies learners' attributes into two groups, which are static and dynamic attributes. Static attributes tend not to change or very rarely change. They include learner's profile, special needs, aptitude, preference, learning approach, learning style, personality, and cognitive capability. On the other hand, dynamic attributes include learning history and study plan. Through conducting some experiments, it can be concluded that LifeOn supports ubiquitous and lifelong learning, and can be used in adaptive learning systems with various learner models.

Cardiovascular disease is currently the leading cause of death in Canada and other developed countries. Physical activity based cardiac rehabilitation programs have been shown to reduce the likelihood of subsequent cardiac events and even reverse the disease process. However, factors influencing physical activity in cardiac patients are still not clearly understood. The overall objective of this dissertation was therefore to better understand motivation and physical activity in a cardiac rehabilitation context. Specifically, theory-based motivational variables were studied as correlates of physical activity. To accomplish this objective, a two-purpose research approach was taken. First, two articles (Article-1 and Article-2) aimed to test and integrate concepts from two strong motivational theories: Self-Efficacy Theory (SET) and Self-Determination Theory (SDT) into one comprehensive model using the novel and rigorous approach of Noar and Zimmerman (2005). The second purpose of this dissertation was to extend the findings from the first purpose by investigating physical activity and motivational patterns over a 24-month period in cardiac patients (Article 3). With regards to the first purpose, Article-1 revealed that the integration of SDT and SET was feasible as the integrated model had good model fit, explained more variance in self-determined motivation, confidence, and physical activity and supported similar number of hypothesised links in a cross-sectional cardiac sample as well two other samples: primary care adults and university students. Due to the cross-sectional nature of Article1, Article-2 tested the integrated SDT-SET model from cardiac patients with longitudinal data of patients following a cardiac rehabilitation program. Although no motivational variables predicted residual change in physical activity at 4-months, this longitudinal model was found to have good model fit. Across both articles, the integration of SDT and SET was found to be possible. However, more research is needed to further test the integration of these theories. As for the

The support for the use of Computer-Supported Collaborative Learning is a sign of contributions and support for in-class and out-of-class learning. This study investigates the perspective of student’s digital multi-modal literacy on student's behavioral intention to use a CSCL. We proposed a theoretical model that examines student perspectives on the integration of digital multi-modal literacy in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study empirically examined and validated the proposed theoretical model based on a digital multi-modal computer-supported collaborative learning adoption. The data were analyzed with a partial least square, structural equation modeling (PLS-SEM) statistical approach. Results suggest that student's perspective on multi-modal literacy has a positive and significant impact on the behavioral intention to use. Furthermore, all the UTAUT factors have a strong and significant impact on the behavioral intention to support the use of digital multi-modal computer-supported collaborative learning. Therefore, student's perspective on multi-modal literacy contributes towards their behavioural intention to use collaborative computer-based learning. To further improve collaboration and communication based on the use of CSCL to support students learning environment.

The integration of Artificial Intelligence (AI) into higher education is significantly transforming traditional pedagogical models, particularly through the emergence of adaptive learning systems. These AI-powered platforms enhance the learning experience by tailoring content delivery to individual student needs, adapting in real time based on performance, learning pace, and engagement. This personalized approach promotes more effective and engaging learning, addressing diverse student abilities and improving overall academic outcomes. This concept paper explores the implementation and impact of AI-driven adaptive learning in higher education across five leading nations: the United States, the United Kingdom, Australia, China, and Singapore. These countries have emerged as frontrunners in educational innovation, each leveraging AI through distinct policy frameworks, institutional strategies, and varying levels of technological maturity. By analysing their approaches, this paper seeks to uncover how universities in these regions are integrating adaptive learning systems, examining the successes they have achieved, the challenges they face, and the lessons they have learned along the way. In doing so, the paper identifies key institutional practices and national strategies that have facilitated effective adoption. It also proposes a conceptual framework to guide higher education institutions in other contexts seeking to implement similar technologies. By synthesizing global experiences and drawing meaningful comparisons, the paper aims to offer practical insights and strategic recommendations. Ultimately, it provides a roadmap for institutions aspiring to harness the power of AI to develop more personalized, responsive, and future-ready learning environments that can meet the evolving needs of students in the 21st century.

A study on the behavior intention of university students participating in winter sports

The integration of consumer behavior models into bank-owned e-commerce strategies has emerged as a critical focal point for enhancing customer engagement, improving transaction volumes, and fostering competitive advantage in the digital economy. This technical review examines the theoretical foundations and practical applications of key consumer behavior models such as the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) within the context of bank-operated e-commerce ecosystems. Banks are increasingly leveraging e-commerce platforms not only as transactional interfaces but also as value-driven ecosystems aimed at providing personalized financial and non-financial services. However, the adoption and sustained usage of these platforms hinge significantly on understanding consumer motivation, perceived value, trust, convenience, and digital literacy. The review explores how predictive behavioral models inform digital platform design, influence user interface optimization, guide recommendation engines, and support omnichannel banking strategies. By mapping consumer psychological and sociocultural factors to technological engagement metrics, banks can better tailor user experiences, segment markets, and optimize conversion pathways. Furthermore, the paper evaluates the integration of machine learning and data analytics in modeling dynamic consumer behaviors and preferences across different demographic and regional segments. Challenges such as data privacy concerns, algorithmic bias, and the digital divide are also addressed, with proposed solutions focusing on ethical AI, inclusive design, and adaptive learning systems. Case studies from leading African and global banks illustrate diverse implementation pathways and outcomes. This review concludes that a data-informed understanding of consumer behavior, when aligned with agile technology architectures, can significantly enhance the effectiveness of bank-owned e-commerce strategies. Future research directions include the co-evolution of behavioral models with emerging technologies like blockchain, embedded finance, and generative AI.

Hybrid cars were developed and are widely utilized in developed countries due to their sustainability advantages. However, developing countries were seen to underutilize this product where research showed that the economic, societal, and environmental concerns were considered by consumers. The current study aimed to assess the behavioral intentions for purchasing hybrid cars with the use of an established new framework called sustainability theory of planned behavior (STPB) and integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. A total of 1048 valid respondents were purposely gathered to completely assess the behavioral, technological, and sustainable domains of purchasing intentions of hybrid cars through structural equation modeling. From the results, the perceived economic concerns and perceived authority support showed the most significant factor indirectly affecting behavioral intention to purchase hybrid cars, followed by perceived environmental concern. All domains under the theory of planned behavior were significant. However, only performance expectancy, hedonic motivation, and price value were deemed significant on the technological aspect. With the underutilization of hybrid cars in the country, habit, effort expectancy, and facilitating conditions were insignificant. The theoretical framework can be used independently (STPB), or as a whole, for the evaluation of purchasing intention of eco-friendly or smart technologies products. This study was also able to provide implications on the behavioral, managerial, and sustainability aspects of hybrid cars. Lastly, the theoretical implications suggested that the sustainability theory of planned behavior may be extended or applied in holistically assessing different product evaluation, industries, and related studies—even in different countries.

The adaptive education frameworks within e-learning platforms are designed to ensure that each learner's learning experience differs. To provide adaptive e-learning programs and research materials adapted to adaptive learning, this type of educational approach attempts to integrate the ability to understand and identify the unique needs of an individual in the context of learning with the skills needed to use suitable learning pedagogy and to improve the learning process. Thus, designing realistic student profiles and templates based on an overview of their affective states, level of experience, and individual personality traits and abilities is essential. The data collected can then be used and used effectively for the creation of an adaptive learning system. These learner models can be used in two ways once learned. The first is to educate the pedagogy of the integrated educational method suggested by the experts and designers. The second objective is to provide the framework dynamic self-learning ability based on teachers and students' behaviours to build effective pedagogy and adapt e-learning environments automatically according to pedagogies. Artificial intelligence algorithms can, for various reasons, be useful, including their ability to improve and mimic human reasoning and decision-making (learning-teaching model) and to reduce the source of uncertainty in order to achieve an effective background of learning. These leadership skills ensure progress for both the learner and the system over the lifelong learning process. In the following document, we present a survey on growing and relevant issues in the field of artificial intelligence algorithms, their advantages and drawbacks (fuzzy logic and A prior algorithm), as well as the importance of using these strategies to make e-learning system smarter and more efficient.

This study examines the impact of Adaptive AI learning systems among learning-disabled students. In most cases, there are students who are having learning problems; hence, in a typical classroom, they cannot cope with the lessons offered in the classroom as the pace is too fast. They are excluded most of the time, lack motivation, and fear to pose questions. solution to this issue, special education and inclusive classrooms deployed adaptive AI learning systems. These software control lessons are according to the level of students and provide individual feedback. This study has been conducted on a sample of 180 students aged 10 to 16 years. They were set in two equal groups. Adaptive AI learning systems were applied to one group, and for the other group, the teaching method was pursued. The research design was quantitative and experimental. Academic performance scores, motivation and engagement tests, and independent learning skill checklists were used to collect data. The findings indicated that students using AI systems scored better in their schoolwork compared to the control group. The group of AI students became more motivated, interested, and active in the classroom as well. They were stronger and more self-reliant in studying. The students corrected their mistakes as quickly as possible and had fun due to real-time feedback and playful-interactive lessons. These results are consistent with the previous studies noting the advantages of AI tools in learning. In this work, it is proposed that schools are to implement adaptive AI systems as a way of enhancing learning achievement in students with disabilities. It is advised to conduct future research and evaluate such systems in other schools and on other kinds of learning difficulties.

Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

User acceptance of autonomous public transport systems: Extended UTAUT2 model

This study aims to analyze the determinants of intention to use the Quick Response Code Indonesian Standard (QRIS) on mobile banking users of Islamic banking by integrating the religiosity variable as a moderator in the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model to predict user intentions and behavior. UTAUT3 consists of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habits, and personal innovativeness as determinants of intention and behavior using QRIS mobile banking. A total of 255 respondents were selected as samples using the purposive sampling method. This study uses a quantitative approach with SEM-PLS (Structural Equation Modeling - Partial Least Squares) analysis using SmartPLS software version 3.0. Empirical results show that social influence, facilitating conditions, price value, habits, and personal innovativeness significantly positively affect behavioral intention to use QRIS mobile banking in Islamic banking. Conversely, performance expectancy, effort expectancy, and hedonic motivation do not have a significant effect on this intention. In addition, behavioral intention, habits, and personal innovativeness are proven to have a significant positive influence on the actual behavior of using QRIS mobile banking in Islamic banking. However, facilitating conditions do not have a significant effect on usage behavior. Religiosity as a moderating variable is unable to moderate the relationship between behavioral intention and actual behavior of using QRIS. The implications of this study are to provide insight for the Islamic banking industry to develop marketing strategies based on social values and personal innovativeness and to facilitate users with conditions that support QRIS adoption. This study also contributes to developing consumer behavior theory through a more comprehensive integration of UTAUT3 in the context of Islamic banking.